void SetUpNeurons()
{
if (Brain == null)
{
return;
}
int i = 0;
EyeR_I = GetListSliceAsArray(i, i += EyeNeuronsAmount, Brain.Inputs);
EyeG_I = GetListSliceAsArray(i, i += EyeNeuronsAmount, Brain.Inputs);
EyeB_I = GetListSliceAsArray(i, i += EyeNeuronsAmount, Brain.Inputs);
EyeDistance_I = GetListSliceAsArray(i, i += EyeNeuronsAmount, Brain.Inputs);
random_I = Brain.Inputs[i++];
clock_I = Brain.Inputs[i++];
energy_I = Brain.Inputs[i++];
i = 0;
rotate_O = Brain.Outputs[i++];
move_O = Brain.Outputs[i++];
eat_O = Brain.Outputs[i++];
mate_O = Brain.Outputs[i++];
attack_O = Brain.Outputs[i++];
i = 0;
//BodyGenes
}
public void ApplyInputs_WithValidNetwork_PropogatesNetworkInputs()
{
// Arrange
var network = new DFFNeuralNetwork(1, 1, 2, 2);
var inputs = new List <INetworkInput>()
{
new NetworkInput()
{
ActivationLevel = .75
}
};
network.RandomizeNetwork();
IInputNeuron input1 = network.Layers.OfType <IInputLayer>().First().Neurons.First() as IInputNeuron;
IOutgoingConnection input1Hidden1Conn = input1.Connections.OfType <IOutgoingConnection>().First();
IOutgoingConnection input1Hidden2Conn = input1.Connections.OfType <IOutgoingConnection>().ToList()[1];
IHiddenNeuron hidden1 = input1Hidden1Conn.ToNeuron as IHiddenNeuron;
IOutgoingConnection hidden1Output1Conn = hidden1.Connections.OfType <IOutgoingConnection>().First();
IOutgoingConnection hidden1Output2Conn = hidden1.Connections.OfType <IOutgoingConnection>().ToList()[1];
IHiddenNeuron hidden2 = input1Hidden2Conn.ToNeuron as IHiddenNeuron;
IOutgoingConnection hidden2Output1Conn = hidden2.Connections.OfType <IOutgoingConnection>().First();
IOutgoingConnection hidden2Output2Conn = hidden2.Connections.OfType <IOutgoingConnection>().ToList()[1];
IOutputNeuron output1 = hidden1Output1Conn.ToNeuron as IOutputNeuron;
IOutputNeuron output2 = hidden1Output2Conn.ToNeuron as IOutputNeuron;
// Act
network.ApplyInputs(inputs);
var outputLayer = network.Layers.OfType <IOutputLayer>().First();
IOutputNeuron output1After = outputLayer.Neurons.First() as IOutputNeuron;
IOutputNeuron output2After = outputLayer.Neurons.ToList()[1] as IOutputNeuron;
// sigmoid function : (1.0 / (1 + Math.Exp(-1.0 * value)));
var h1Activation = ApplyActivationFunction((.75 * input1Hidden1Conn.Weight) + hidden1.Bias);
var h2Activation = ApplyActivationFunction((.75 * input1Hidden2Conn.Weight) + hidden2.Bias);
var o1Activation = ApplyActivationFunction((h1Activation * hidden1Output1Conn.Weight) + (h2Activation * hidden2Output1Conn.Weight) + output1.Bias);
var o2Activation = ApplyActivationFunction((h1Activation * hidden1Output2Conn.Weight) + (h2Activation * hidden2Output2Conn.Weight) + output2.Bias);
// Assert
Assert.IsTrue(o1Activation == output1After.ActivationLevel);
Assert.IsTrue(o2Activation == output2After.ActivationLevel);
}
/// <summary>
/// Creates the incoming and outgoing connections between this neuron and a output neuron.
/// </summary>
/// <param name="neuron">The output neuron to connect this neuron to.</param>
public void GenerateConnectionsWith(IOutputNeuron neuron)
{
if (neuron == null)
{
throw new ArgumentNullException("neuron");
}
// Create connection only if this neuron doesn't already have an outgoing connection to the output neuron.
if (!GetOutgoingConnections().Any(c => c.ToNeuron == neuron))
{
Connections.Add(new OutgoingConnection(neuron));
}
// Create connection only if the output neuron doesn't already have an incoming connection from this neuron.
if (!neuron.GetIncomingConnections().Any(c => c.FromNeuron == this))
{
neuron.Connections.Add(new IncomingConnection(this));
}
}
public override void Load(BinaryReader r, uint id)
{
base.Load(r, id);
Brain = ReadNullableObject <INeuralNet>(r);
CreatureGenome = ReadNullableObject <Genome>(r);
EyeNeuronsAmount = r.ReadInt32();
EyeSpan = r.ReadDouble();
ViewDistance = r.ReadInt32();
Speed = r.ReadDouble();
RotationSpeed = r.ReadDouble();
Energy = DoubleMinMax.Read(r);
CanSeeCreatures = r.ReadBoolean();
EnergyLossByTick = r.ReadDouble();
EnergyLossByRotation = r.ReadDouble();
EnergyLossByMove = r.ReadDouble();
EnergyLossByEating = r.ReadDouble();
EnergyLossByMating = r.ReadDouble();
EatingBiteSize = r.ReadDouble();
MatingCandidate = BinarySerializable.GetObject <Creature>(r);
CyclesInMating = r.ReadInt32();
CyclesNeededForMating = r.ReadInt32();
MatingCooldownCyclesLeft = r.ReadInt32();
MatingCooldown = r.ReadInt32();
AttackingCooldownCyclesLeft = r.ReadInt32();
AttackingCooldown = r.ReadInt32();
AttackMultiplier = r.ReadDouble();
MaxAge = r.ReadInt32();
PixelsRotatedLeft = r.ReadDouble();
PixelsRotatedRight = r.ReadDouble();
PixelsMovedForwards = r.ReadDouble();
PixelsMovedBackwards = r.ReadDouble();
TimesHaveEaten = r.ReadInt32();
TimesMated = r.ReadInt32();
LoseEnergy = r.ReadBoolean();
IsThinking = r.ReadBoolean();
DisableMating = r.ReadBoolean();
DisableAttacking = r.ReadBoolean();
AlwaysMate = r.ReadBoolean();
AlwaysEat = r.ReadBoolean();
if (Brain != null)
{
EyeR_I = LoadArrayInfo <IInputNeuron>(r);
EyeG_I = LoadArrayInfo <IInputNeuron>(r);
EyeB_I = LoadArrayInfo <IInputNeuron>(r);
EyeDistance_I = LoadArrayInfo <IInputNeuron>(r);
random_I = BinarySerializable.GetObject <IInputNeuron>(r);
clock_I = BinarySerializable.GetObject <IInputNeuron>(r);
energy_I = BinarySerializable.GetObject <IInputNeuron>(r);
rotate_O = BinarySerializable.GetObject <IOutputNeuron>(r);
move_O = BinarySerializable.GetObject <IOutputNeuron>(r);
eat_O = BinarySerializable.GetObject <IOutputNeuron>(r);
mate_O = BinarySerializable.GetObject <IOutputNeuron>(r);
attack_O = BinarySerializable.GetObject <IOutputNeuron>(r);
}
}
public void Initialize()
{
_inputNeuron = new InputNeuron();
_hiddenNeuron = new HiddenNeuron();
_outputNeuron = new OutputNeuron();
}
